Past Meetup

March BARUG Meeting at Predictive Analytics World

This Meetup is past

170 people went

Location image of event venue

Details

We are pleased to announce that the March BARUG meeting will be held in conjunction with Predictive Analytics World 2012. The meeting will be held on Monday, March 5th from 7:30PM to 10:00 PM at the San Francisco Marriott Marquis Hotel 55 Fourth Street San Francisco, CA 94103

Gaining access to the exhibit hall, and to the reception that will take place there, as well as to the BARUG meeting itself will require that BARUG members have a badge.

Please register here (https://www.eiseverywhere.com/ereg/index.php?eventid=27185&categoryid=190747) to get your badge.

The preliminary agenda is:

6:10 to 7:30PM reception in exhibit hall

7:45 to 7:55PM Announcements

8:00 to 8:20PM David Smith will describe the winning entries in a recent contest (http://www.revolutionanalytics.com/news-events/news-room/2012/applications-of-r-in-business-competition.php) sponsored by Revolution Analytics to develop Business Applications in R. Entries include market forecasting, sentiment analysis, clinical trial forecasting and more

8:25 to 9:30PM Michael Lawrence of Genetech: Interactive Graphics for Large Data in R

Creating an interactive visualization for large data, on the scale of
millions or more, requires attention to both the design and
implementation of the graphics. This talk is concerned with the
latter, in particular software infrastructure for fast, scalable
interactive graphics. The implementation of an interactive
visualization consists of both drawing and computation (filtering,
summarizing, transforming, etc), and a graphics system needs to scale
in both respects. For drawing, we have developed a
hardware-accelerated renderer, with multi-layered buffers for
incremental updating and efficient spatial algorithms for mapping user
actions to the data. It is low-level and applicable to a variety of
graphics problems. Towards scalable computation, we have applied the
model-view-controller pattern to the design of computational pipelines
that operate dynamically on subsets of the data and make feasible
the flexible and rapid development of algorithms implemented in the R
language.